Strengthening Customer Loyalty via Data Discovery

One of the strengths of analytics tools is their ability to reveal previously hidden insights about customers to help companies strengthen their relationships with those customers.

This includes mining information that customers share in social media channels and contact center interactions that can uncover the types of things that matter most to customers (e.g., timely service, consistent experiences, private shopping opportunities).

Strategies for attracting and retaining customers are much simpler when efforts to drive repeat business are largely centered around segmenting customers and creating the right incentives and rewards to earn loyalty, as a recent article in Information Management highlights.

But so much has changed with the digital revolution. Now, customers have an incredible amount of choice between which companies to do business with and the channels they can use to interact with them, all of which is taking a toll on loyalty. According to an Accenture survey of consumers across 10 industries, one in five consumers switched companies in 2012, up 5% from the previous year.

One industry that could benefit significantly by paying greater attention to the things that matter most to customers is financial services.

Now, nearly four years after the start of the global economic meltdown, fewer than half of customers (46%) say they trust financial services companies, according to the 2012 Edelman Trust Barometer. Although this figure represents an improvement from a low point of 25% in 2011, banks and other financial services companies still have a long way to go toward re-establishing trust with customers.

There are several ways that financial services companies can use financial analytics to act on customer sentiment to ensure that they’re taking the steps necessary to deliver on the aspects of the customer experience that matter most to them, according to a recent interview with Gallup chief economist Dennis Jacobe.

As such, when bank executives examine potential areas for cost reduction, they should steer away from considering cuts to front-line personnel. With fewer people handling consumer issues, customers are more likely to face longer wait times, which can result in lower customer satisfaction rates and higher churn rates, especially among customers who will jump to other banks they feel can provide better services.

Retailers and companies in other industries can also benefit from the use of data discovery to identify common characteristics that are shared by certain customer segments (e.g., high-value, low-frequency shoppers) and how those traits correlate to certain actions such as defecting to other companies.

Of course, being able to pinpoint indicators of customer churn, even in real time, can sometimes occur too late for a company to act on or manage to dissuade a customer who has already decided to jump ship.

A more effective approach is to use customer data and analytics to determine the types of offers, messaging, service experiences, and content that are most likely to keep certain customers in the fold based on responses from other customers with similar characteristics and demonstrated behaviors (e.g., product usage, purchase history, channel preferences, lifecycle status, and other demographic and behavioral information).

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